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Tech Stack
Tools & technologiesAWSAzureCloudGoogle Cloud PlatformPythonPyTorchTensorflow
About the role
Key responsibilities & impact- Work with stakeholders to define machine learning solution designs based on cloud services such as Azure and Snowflake.
- Design, build, and maintain machine learning pipelines and frameworks to support enterprise analytics, reporting, and product needs.
- Collaborate with data science, ML engineering, data quality, and product teams on model deployment architecture and implementation.
- Manage, configure, and optimize cloud environments and related machine learning services.
- Implement tools and processes for model integration, storage, profiling, monitoring, processing, management, and archival.
- Support enterprise-wide model governance, performance tracking, and lifecycle management standards.
- Recommend improvements to ML platforms, tools, and development practices to support strategic technology and business objectives.
- Work with SaaS vendors and strategic partners to implement and maintain modern machine learning solutions.
- Use Agile practices to manage delivery, contribute to project planning, and support successful rollout of ML products.
- Partner with internal stakeholders to understand business requirements and translate them into reliable ML operations solutions.
- Stay current with emerging MLOps tools, cloud technologies, and best practices to ensure solutions remain scalable, secure, and fit for purpose.
Requirements
What you’ll need- Bachelor's degree in Computer Science, Engineering or Technical Field preferred.
- Minimum 3-7 years of relevant experience.
- Proven experience in machine learning engineering and operations.
- Profound understanding of machine learning concepts, model lifecycle management, and experience in model management capabilities including model definitions, performance management and integration.
- Execution of model deployment, monitoring, profiling, governance and analysis initiatives.
- Excellent interpersonal, oral, and written communication; Ability to relate ideas and concepts to others; write reports, business correspondence, project plans and procedure documents.
- Solid Python, ML frameworks (e.g., TensorFlow, PyTorch), data modeling, and programming skills.
- Experience and strong understanding of cloud architecture and design (AWS, Azure, GCP).
- Experience using modern approaches to automating machine learning pipelines.
- Agile and Waterfall methodologies.
- Ability to work independently and manage multiple task assignments within a structured implementation methodology.
- Personally invested in continuous improvement and innovation.
- Motivated, self-directed individual that works well with minimal supervision.
- Must have experience working across multiple teams/technologies.
- Preferred but not essential: Experience with business intelligence tools (preferably PowerBI).
- Preferred but not essential: Experience with MLOps tools (e.g., MLflow, Kubeflow).
Benefits
Comp & perks- Work on global projects with clients from worldwide.
- Be part of a remote-first culture-work from anywhere with flexibility.
- Enjoy team-building activities and regular outings.
- Collaborate and grow in a supportive environment with opportunities to learn from senior engineers.
- Competitive salary and benefits package.
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learning engineeringmodel lifecycle managementmodel deploymentmonitoringprofilinggovernancePythonTensorFlowPyTorchcloud architecture
Soft Skills
interpersonal communicationoral communicationwritten communicationproject planningability to work independentlycontinuous improvementinnovationself-directedcollaborationability to manage multiple tasks
Certifications
Bachelor's degree in Computer ScienceBachelor's degree in EngineeringBachelor's degree in Technical Field
